Colon FACS Notebook
Enter the directory of the maca folder on your drive and the name of the tissue you want to analyze.
tissue_of_interest = "Colon"Load the requisite packages and some additional helper functions.
library(here)
library(useful)
library(Seurat)
library(dplyr)
library(Matrix)
save_dir = here('00_data_ingest', 'tissue_robj')# read the metadata to get the plates we want
plate_metadata_filename = here('00_data_ingest', 'facs_raw_data', 'metadata_FACS.csv')
plate_metadata <- read.csv(plate_metadata_filename, sep=",", header = TRUE)
colnames(plate_metadata)[1] <- "plate.barcode"
plate_metadataSubset the metadata on the tissue.
tissue_plates = filter(plate_metadata, tissue == tissue_of_interest)[,c('plate.barcode','tissue','subtissue','mouse.sex')]
tissue_platesLoad the read count data.
#Load the gene names and set the metadata columns by opening the first file
filename = here('00_data_ingest', 'facs_raw_data', 'FACS', paste0(tissue_of_interest, '-counts.csv'))
raw.data = read.csv(filename, sep=",", row.names=1)
# raw.data = data.frame(row.names = rownames(raw.data))
corner(raw.data)Make a vector of plate barcodes for each cell
plate.barcodes = lapply(colnames(raw.data), function(x) strsplit(strsplit(x, "_")[[1]][1], '.', fixed=TRUE)[[1]][2])
head(plate.barcodes)[[1]]
[1] "MAA001871"
[[2]]
[1] "MAA001871"
[[3]]
[1] "MAA001871"
[[4]]
[1] "MAA001871"
[[5]]
[1] "MAA001871"
[[6]]
[1] "MAA001871"Use only the metadata rows corresponding to Bladder plates. Make a plate barcode dataframe to “expand” the per-plate metadata to be per-cell.
head(raw.data)barcode.df = t.data.frame(as.data.frame(plate.barcodes))
rownames(barcode.df) = colnames(raw.data)
colnames(barcode.df) = c('plate.barcode')
head(barcode.df) plate.barcode
F11.MAA001871.3_39_F.1.1 "MAA001871"
G16.MAA001871.3_39_F.1.1 "MAA001871"
I1.MAA001871.3_39_F.1.1 "MAA001871"
A13.MAA001871.3_39_F.1.1 "MAA001871"
B16.MAA001871.3_39_F.1.1 "MAA001871"
C18.MAA001871.3_39_F.1.1 "MAA001871" rnames = row.names(barcode.df)
meta.data <- merge(barcode.df, plate_metadata, by='plate.barcode', sort = F)
row.names(meta.data) <- rnames
# Sort by plate barcode because that's how the data was originally
meta.data = meta.data[order(meta.data$plate.barcode), ]
raw.data = raw.data[, rownames(meta.data)]
head(raw.data)Process the raw data and load it into the Seurat object.
# Find ERCC's, compute the percent ERCC, and drop them from the raw data.
erccs <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = TRUE)
percent.ercc <- Matrix::colSums(raw.data[erccs, ])/Matrix::colSums(raw.data)
ercc.index <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = FALSE)
raw.data <- raw.data[-ercc.index,]
# Create the Seurat object with all the data
tiss <- CreateSeuratObject(raw.data = raw.data, project = tissue_of_interest,
min.cells = 5, min.genes = 5)
tiss <- AddMetaData(object = tiss, meta.data)
tiss <- AddMetaData(object = tiss, percent.ercc, col.name = "percent.ercc")
# Change default name for sums of counts from nUMI to nReads
colnames(tiss@meta.data)[colnames(tiss@meta.data) == 'nUMI'] <- 'nReads'
# Create metadata columns for annotations and subannotations
tiss@meta.data[,'annotation'] <- NA
tiss@meta.data[,'subannotation'] <- NACalculate percent ribosomal genes.
ribo.genes <- grep(pattern = "^Rp[sl][[:digit:]]", x = rownames(x = tiss@data), value = TRUE)
percent.ribo <- Matrix::colSums(tiss@raw.data[ribo.genes, ])/Matrix::colSums(tiss@raw.data)
tiss <- AddMetaData(object = tiss, metadata = percent.ribo, col.name = "percent.ribo")A sanity check: genes per cell vs reads per cell.
GenePlot(object = tiss, gene1 = "nReads", gene2 = "nGene", use.raw=T)
Filter out cells with few reads and few genes.
tiss <- FilterCells(object = tiss, subset.names = c("nGene", "nReads"),
low.thresholds = c(500, 50000), high.thresholds = c(25000, 2000000))Normalize the data, then regress out correlation with total reads
tiss <- NormalizeData(object = tiss)Performing log-normalization
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|----|----|----|----|----|----|----|----|----|----|
**************************************************|Run Principal Component Analysis.
tiss <- RunPCA(object = tiss, do.print = FALSE)
tiss <- ProjectPCA(object = tiss, do.print = FALSE)
Later on (in FindClusters and TSNE) you will pick a number of principal components to use. This has the effect of keeping the major directions of variation in the data and, ideally, supressing noise. There is no correct answer to the number to use, but a decent rule of thumb is to go until the plot plateaus.
PCElbowPlot(object = tiss)
Choose the number of principal components to use.
# Set number of principal components.
n.pcs = 20The clustering is performed based on a nearest neighbors graph. Cells that have similar expression will be joined together. The Louvain algorithm looks for groups of cells with high modularity–more connections within the group than between groups. The resolution parameter determines the scale…higher resolution will give more clusters, lower resolution will give fewer.
For the top-level clustering, aim to under-cluster instead of over-cluster. It will be easy to subset groups and further analyze them below.
# Set resolution
res.used <- 0.5
tiss <- FindClusters(object = tiss, reduction.type = "pca", dims.use = 1:n.pcs,
resolution = res.used, print.output = 0, save.SNN = TRUE)To visualize
# If cells are too spread out, you can raise the perplexity. If you have few cells, try a lower perplexity (but never less than 10).
tiss <- RunTSNE(object = tiss, dims.use = 1:n.pcs, seed.use = 10, perplexity=30)# note that you can set do.label=T to help label individual clusters
TSNEPlot(object = tiss, do.label = T)
Check expression of genes of interset.
#{r, echo=FALSE, fig.height=16, fig.width=12}
genes_to_check = c('Lgr5', 'Krt20', 'Atoh1', 'Chga', 'Dclk1', 'Mki67')
#genes_to_check = c('Alb', 'Cyp2f2', 'Cyp2e1', 'Hamp')
#FeaturePlot(tiss, genes_to_check, pt.size = 1, nCol = 3)
FeaturePlot(tiss, genes_to_check, pt.size = 1)
Dotplots let you see the intensity of exppression and the fraction of cells expressing for each of your genes of interest.
# To change the y-axis to show raw counts, add use.raw = T.
VlnPlot(tiss, genes_to_check)
# To change the y-axis to show raw counts, add use.raw = T.
VlnPlot(tiss, 'Lgr5')
# To get plots of two genes, add use.raw = T.
GenePlot(tiss, 'Krt20', 'Atoh1')
How big are the clusters?
table(tiss@ident)
0 1 2 3 4 5 6 7 8 9 10 11 12
988 664 583 231 220 220 200 153 143 133 132 55 25 Which markers identify a specific cluster?
clust.markers <- FindMarkers(object = tiss, ident.1 = 0, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 39sprint(x = head(x= clust.markers, n = 10))You can also compute all markers for all clusters at once. This may take some time.
tiss.markers <- FindAllMarkers(object = tiss, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
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|+++ | 4 % ~57s
|+++ | 5 % ~57s
|++++ | 6 % ~57s
|++++ | 7 % ~57s
|+++++ | 8 % ~57s
|+++++ | 9 % ~56s
|++++++ | 11% ~56s
|++++++ | 12% ~55s
|+++++++ | 13% ~55s
|+++++++ | 14% ~54s
|++++++++ | 15% ~53s
|++++++++ | 16% ~52s
|+++++++++ | 17% ~51s
|+++++++++ | 18% ~51s
|++++++++++ | 19% ~50s
|++++++++++ | 20% ~49s
|+++++++++++ | 21% ~48s
|++++++++++++ | 22% ~48s
|++++++++++++ | 23% ~48s
|+++++++++++++ | 24% ~47s
|+++++++++++++ | 25% ~46s
|++++++++++++++ | 26% ~46s
|++++++++++++++ | 27% ~45s
|+++++++++++++++ | 28% ~44s
|+++++++++++++++ | 29% ~43s
|++++++++++++++++ | 31% ~43s
|++++++++++++++++ | 32% ~42s
|+++++++++++++++++ | 33% ~42s
|+++++++++++++++++ | 34% ~41s
|++++++++++++++++++ | 35% ~40s
|++++++++++++++++++ | 36% ~40s
|+++++++++++++++++++ | 37% ~39s
|+++++++++++++++++++ | 38% ~38s
|++++++++++++++++++++ | 39% ~38s
|++++++++++++++++++++ | 40% ~37s
|+++++++++++++++++++++ | 41% ~36s
|++++++++++++++++++++++ | 42% ~36s
|++++++++++++++++++++++ | 43% ~35s
|+++++++++++++++++++++++ | 44% ~34s
|+++++++++++++++++++++++ | 45% ~34s
|++++++++++++++++++++++++ | 46% ~33s
|++++++++++++++++++++++++ | 47% ~32s
|+++++++++++++++++++++++++ | 48% ~32s
|+++++++++++++++++++++++++ | 49% ~31s
|++++++++++++++++++++++++++ | 51% ~30s
|++++++++++++++++++++++++++ | 52% ~30s
|+++++++++++++++++++++++++++ | 53% ~29s
|+++++++++++++++++++++++++++ | 54% ~28s
|++++++++++++++++++++++++++++ | 55% ~28s
|++++++++++++++++++++++++++++ | 56% ~27s
|+++++++++++++++++++++++++++++ | 57% ~27s
|+++++++++++++++++++++++++++++ | 58% ~26s
|++++++++++++++++++++++++++++++ | 59% ~25s
|++++++++++++++++++++++++++++++ | 60% ~25s
|+++++++++++++++++++++++++++++++ | 61% ~24s
|++++++++++++++++++++++++++++++++ | 62% ~23s
|++++++++++++++++++++++++++++++++ | 63% ~23s
|+++++++++++++++++++++++++++++++++ | 64% ~22s
|+++++++++++++++++++++++++++++++++ | 65% ~22s
|++++++++++++++++++++++++++++++++++ | 66% ~21s
|++++++++++++++++++++++++++++++++++ | 67% ~20s
|+++++++++++++++++++++++++++++++++++ | 68% ~20s
|+++++++++++++++++++++++++++++++++++ | 69% ~19s
|++++++++++++++++++++++++++++++++++++ | 71% ~18s
|++++++++++++++++++++++++++++++++++++ | 72% ~18s
|+++++++++++++++++++++++++++++++++++++ | 73% ~17s
|+++++++++++++++++++++++++++++++++++++ | 74% ~16s
|++++++++++++++++++++++++++++++++++++++ | 75% ~16s
|++++++++++++++++++++++++++++++++++++++ | 76% ~15s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~14s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~14s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~13s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~12s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~12s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~11s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~11s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~10s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~09s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~09s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 03s
| | 0 % ~calculating
|+ | 1 % ~01m 46s
|++ | 2 % ~01m 48s
|++ | 3 % ~01m 47s
|+++ | 4 % ~01m 46s
|+++ | 5 % ~01m 45s
|++++ | 6 % ~01m 43s
|++++ | 7 % ~01m 42s
|+++++ | 8 % ~01m 40s
|+++++ | 9 % ~01m 39s
|++++++ | 10% ~01m 38s
|++++++ | 11% ~01m 37s
|+++++++ | 12% ~01m 37s
|+++++++ | 13% ~01m 36s
|++++++++ | 14% ~01m 35s
|++++++++ | 15% ~01m 33s
|+++++++++ | 16% ~01m 32s
|+++++++++ | 17% ~01m 31s
|++++++++++ | 18% ~01m 30s
|++++++++++ | 19% ~01m 29s
|+++++++++++ | 20% ~01m 28s
|+++++++++++ | 21% ~01m 26s
|++++++++++++ | 22% ~01m 25s
|++++++++++++ | 23% ~01m 24s
|+++++++++++++ | 24% ~01m 23s
|+++++++++++++ | 25% ~01m 22s
|++++++++++++++ | 26% ~01m 21s
|++++++++++++++ | 27% ~01m 20s
|+++++++++++++++ | 28% ~01m 19s
|+++++++++++++++ | 29% ~01m 17s
|++++++++++++++++ | 30% ~01m 16s
|++++++++++++++++ | 31% ~01m 15s
|+++++++++++++++++ | 32% ~01m 14s
|+++++++++++++++++ | 33% ~01m 13s
|++++++++++++++++++ | 34% ~01m 12s
|++++++++++++++++++ | 35% ~01m 11s
|+++++++++++++++++++ | 36% ~01m 09s
|+++++++++++++++++++ | 37% ~01m 08s
|++++++++++++++++++++ | 38% ~01m 07s
|++++++++++++++++++++ | 39% ~01m 06s
|+++++++++++++++++++++ | 40% ~01m 05s
|+++++++++++++++++++++ | 41% ~01m 04s
|++++++++++++++++++++++ | 42% ~01m 03s
|++++++++++++++++++++++ | 43% ~01m 02s
|+++++++++++++++++++++++ | 44% ~01m 00s
|+++++++++++++++++++++++ | 45% ~59s
|++++++++++++++++++++++++ | 46% ~58s
|++++++++++++++++++++++++ | 47% ~57s
|+++++++++++++++++++++++++ | 48% ~56s
|+++++++++++++++++++++++++ | 49% ~55s
|++++++++++++++++++++++++++ | 51% ~54s
|++++++++++++++++++++++++++ | 52% ~53s
|+++++++++++++++++++++++++++ | 53% ~52s
|+++++++++++++++++++++++++++ | 54% ~51s
|++++++++++++++++++++++++++++ | 55% ~49s
|++++++++++++++++++++++++++++ | 56% ~48s
|+++++++++++++++++++++++++++++ | 57% ~47s
|+++++++++++++++++++++++++++++ | 58% ~46s
|++++++++++++++++++++++++++++++ | 59% ~45s
|++++++++++++++++++++++++++++++ | 60% ~44s
|+++++++++++++++++++++++++++++++ | 61% ~43s
|+++++++++++++++++++++++++++++++ | 62% ~42s
|++++++++++++++++++++++++++++++++ | 63% ~41s
|++++++++++++++++++++++++++++++++ | 64% ~40s
|+++++++++++++++++++++++++++++++++ | 65% ~39s
|+++++++++++++++++++++++++++++++++ | 66% ~37s
|++++++++++++++++++++++++++++++++++ | 67% ~36s
|++++++++++++++++++++++++++++++++++ | 68% ~35s
|+++++++++++++++++++++++++++++++++++ | 69% ~34s
|+++++++++++++++++++++++++++++++++++ | 70% ~33s
|++++++++++++++++++++++++++++++++++++ | 71% ~32s
|++++++++++++++++++++++++++++++++++++ | 72% ~31s
|+++++++++++++++++++++++++++++++++++++ | 73% ~30s
|+++++++++++++++++++++++++++++++++++++ | 74% ~29s
|++++++++++++++++++++++++++++++++++++++ | 75% ~27s
|++++++++++++++++++++++++++++++++++++++ | 76% ~26s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~25s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~24s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~23s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~22s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~21s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~20s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~19s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~18s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~17s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~15s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~14s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~12s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 50s
| | 0 % ~calculating Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|+ | 1 % ~02m 02s
|++ | 2 % ~01m 49s
|++ | 3 % ~01m 49s
|+++ | 4 % ~01m 45s
|+++ | 5 % ~01m 42s
|++++ | 6 % ~01m 39s
|++++ | 7 % ~01m 36s
|+++++ | 8 % ~01m 35s
|+++++ | 9 % ~01m 34s Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|++++++ | 11% ~01m 33s
|++++++ | 12% ~01m 31s
|+++++++ | 13% ~01m 28s
|+++++++ | 14% ~01m 27s
|++++++++ | 15% ~01m 25s
|++++++++ | 16% ~01m 24s
|+++++++++ | 17% ~01m 22s
|+++++++++ | 18% ~01m 21s
|++++++++++ | 19% ~01m 19s
|++++++++++ | 20% ~01m 17s
|+++++++++++ | 21% ~01m 16s
|++++++++++++ | 22% ~01m 14s
|++++++++++++ | 23% ~01m 12s
|+++++++++++++ | 24% ~01m 11s
|+++++++++++++ | 25% ~01m 09s
|++++++++++++++ | 26% ~01m 08s
|++++++++++++++ | 27% ~01m 07s
|+++++++++++++++ | 28% ~01m 05s
|+++++++++++++++ | 29% ~01m 04s
|++++++++++++++++ | 31% ~01m 03s
|++++++++++++++++ | 32% ~01m 02s
|+++++++++++++++++ | 33% ~01m 01s
|+++++++++++++++++ | 34% ~01m 00s
|++++++++++++++++++ | 35% ~59s
|++++++++++++++++++ | 36% ~58s
|+++++++++++++++++++ | 37% ~57s
|+++++++++++++++++++ | 38% ~56s
|++++++++++++++++++++ | 39% ~55s
|++++++++++++++++++++ | 40% ~54s
|+++++++++++++++++++++ | 41% ~53s
|++++++++++++++++++++++ | 42% ~52s
|++++++++++++++++++++++ | 43% ~51s
|+++++++++++++++++++++++ | 44% ~50s
|+++++++++++++++++++++++ | 45% ~49s
|++++++++++++++++++++++++ | 46% ~48s
|++++++++++++++++++++++++ | 47% ~47s
|+++++++++++++++++++++++++ | 48% ~46s
|+++++++++++++++++++++++++ | 49% ~45s
|++++++++++++++++++++++++++ | 51% ~44s
|++++++++++++++++++++++++++ | 52% ~42s
|+++++++++++++++++++++++++++ | 53% ~41s
|+++++++++++++++++++++++++++ | 54% ~40s
|++++++++++++++++++++++++++++ | 55% ~39s
|++++++++++++++++++++++++++++ | 56% ~38s
|+++++++++++++++++++++++++++++ | 57% ~37s
|+++++++++++++++++++++++++++++ | 58% ~36s
|++++++++++++++++++++++++++++++ | 59% ~35s
|++++++++++++++++++++++++++++++ | 60% ~34s
|+++++++++++++++++++++++++++++++ | 61% ~34s
|++++++++++++++++++++++++++++++++ | 62% ~33s
|++++++++++++++++++++++++++++++++ | 63% ~32s
|+++++++++++++++++++++++++++++++++ | 64% ~31s
|+++++++++++++++++++++++++++++++++ | 65% ~30s
|++++++++++++++++++++++++++++++++++ | 66% ~29s
|++++++++++++++++++++++++++++++++++ | 67% ~28s
|+++++++++++++++++++++++++++++++++++ | 68% ~27s
|+++++++++++++++++++++++++++++++++++ | 69% ~26s
|++++++++++++++++++++++++++++++++++++ | 71% ~25s
|++++++++++++++++++++++++++++++++++++ | 72% ~24s
|+++++++++++++++++++++++++++++++++++++ | 73% ~23s
|+++++++++++++++++++++++++++++++++++++ | 74% ~22s
|++++++++++++++++++++++++++++++++++++++ | 75% ~21s
|++++++++++++++++++++++++++++++++++++++ | 76% ~21s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~20s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~19s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~18s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~17s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~16s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~15s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~14s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~13s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~12s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~12s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~05s Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 24s
| | 0 % ~calculating
|+ | 1 % ~01m 45s
|++ | 2 % ~01m 45s
|++ | 3 % ~01m 43s
|+++ | 4 % ~01m 41s Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|+++ | 5 % ~01m 44s
|++++ | 6 % ~01m 42s
|++++ | 7 % ~01m 43s
|+++++ | 8 % ~01m 41s
|+++++ | 9 % ~01m 43s Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|++++++ | 10% ~01m 42s
|++++++ | 11% ~01m 41s
|+++++++ | 12% ~01m 40s
|+++++++ | 13% ~01m 38s
|++++++++ | 14% ~01m 37s Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|++++++++ | 15% ~01m 37s
|+++++++++ | 16% ~01m 35s
|+++++++++ | 17% ~01m 34s
|++++++++++ | 18% ~01m 33s
|++++++++++ | 19% ~01m 31s Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|+++++++++++ | 20% ~01m 31s
|+++++++++++ | 21% ~01m 29s
|++++++++++++ | 22% ~01m 28s
|++++++++++++ | 23% ~01m 27s Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|+++++++++++++ | 24% ~01m 26s
|+++++++++++++ | 25% ~01m 25s
|++++++++++++++ | 26% ~01m 24s
|++++++++++++++ | 27% ~01m 23s
|+++++++++++++++ | 28% ~01m 21s
|+++++++++++++++ | 29% ~01m 20s
|++++++++++++++++ | 30% ~01m 18s
|++++++++++++++++ | 31% ~01m 17s
|+++++++++++++++++ | 32% ~01m 16s
|+++++++++++++++++ | 33% ~01m 14s
|++++++++++++++++++ | 34% ~01m 13s
|++++++++++++++++++ | 35% ~01m 11s
|+++++++++++++++++++ | 36% ~01m 10s
|+++++++++++++++++++ | 37% ~01m 09s
|++++++++++++++++++++ | 38% ~01m 08s
|++++++++++++++++++++ | 39% ~01m 07s Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|+++++++++++++++++++++ | 40% ~01m 05s
|+++++++++++++++++++++ | 41% ~01m 04s
|++++++++++++++++++++++ | 42% ~01m 03s
|++++++++++++++++++++++ | 43% ~01m 02s
|+++++++++++++++++++++++ | 44% ~01m 00s
|+++++++++++++++++++++++ | 45% ~59s
|++++++++++++++++++++++++ | 46% ~58s
|++++++++++++++++++++++++ | 47% ~57s
|+++++++++++++++++++++++++ | 48% ~56s Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|+++++++++++++++++++++++++ | 49% ~55s
|++++++++++++++++++++++++++ | 51% ~54s
|++++++++++++++++++++++++++ | 52% ~52s
|+++++++++++++++++++++++++++ | 53% ~51s
|+++++++++++++++++++++++++++ | 54% ~50s
|++++++++++++++++++++++++++++ | 55% ~49s
|++++++++++++++++++++++++++++ | 56% ~48s
|+++++++++++++++++++++++++++++ | 57% ~47s
|+++++++++++++++++++++++++++++ | 58% ~46s Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|++++++++++++++++++++++++++++++ | 59% ~45s
|++++++++++++++++++++++++++++++ | 60% ~44s
|+++++++++++++++++++++++++++++++ | 61% ~43s
|+++++++++++++++++++++++++++++++ | 62% ~42s
|++++++++++++++++++++++++++++++++ | 63% ~41s
|++++++++++++++++++++++++++++++++ | 64% ~39s
|+++++++++++++++++++++++++++++++++ | 65% ~38s
|+++++++++++++++++++++++++++++++++ | 66% ~37s
|++++++++++++++++++++++++++++++++++ | 67% ~36s
|++++++++++++++++++++++++++++++++++ | 68% ~35s
|+++++++++++++++++++++++++++++++++++ | 69% ~34s
|+++++++++++++++++++++++++++++++++++ | 70% ~33s
|++++++++++++++++++++++++++++++++++++ | 71% ~32s Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|++++++++++++++++++++++++++++++++++++ | 72% ~31s
|+++++++++++++++++++++++++++++++++++++ | 73% ~29s
|+++++++++++++++++++++++++++++++++++++ | 74% ~28s
|++++++++++++++++++++++++++++++++++++++ | 75% ~27s
|++++++++++++++++++++++++++++++++++++++ | 76% ~26s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~25s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~24s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~23s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~22s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~21s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~20s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~19s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~18s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~17s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~15s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~14s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~12s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~07s Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 48sWarning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
| | 0 % ~calculating
|+ | 1 % ~01m 52s Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|++ | 2 % ~01m 59s
|++ | 3 % ~01m 54s
|+++ | 4 % ~01m 50s
|+++ | 5 % ~01m 48s
|++++ | 6 % ~01m 45s
|++++ | 7 % ~01m 42s
|+++++ | 8 % ~01m 42s
|+++++ | 9 % ~01m 40s
|++++++ | 10% ~01m 38s
|++++++ | 11% ~01m 36s
|+++++++ | 12% ~01m 34s
|+++++++ | 13% ~01m 32s
|++++++++ | 14% ~01m 31s
|++++++++ | 15% ~01m 30s Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|+++++++++ | 16% ~01m 30s
|+++++++++ | 18% ~01m 29s
|++++++++++ | 19% ~01m 28s
|++++++++++ | 20% ~01m 27s
|+++++++++++ | 21% ~01m 26s
|+++++++++++ | 22% ~01m 25s
|++++++++++++ | 23% ~01m 24s
|++++++++++++ | 24% ~01m 23s
|+++++++++++++ | 25% ~01m 22s
|+++++++++++++ | 26% ~01m 21s
|++++++++++++++ | 27% ~01m 20s
|++++++++++++++ | 28% ~01m 19s
|+++++++++++++++ | 29% ~01m 18s
|+++++++++++++++ | 30% ~01m 17s
|++++++++++++++++ | 31% ~01m 16s
|++++++++++++++++ | 32% ~01m 15s
|+++++++++++++++++ | 33% ~01m 14s
|++++++++++++++++++ | 34% ~01m 12s
|++++++++++++++++++ | 35% ~01m 11s
|+++++++++++++++++++ | 36% ~01m 10s
|+++++++++++++++++++ | 37% ~01m 09s
|++++++++++++++++++++ | 38% ~01m 07s
|++++++++++++++++++++ | 39% ~01m 06s
|+++++++++++++++++++++ | 40% ~01m 05s
|+++++++++++++++++++++ | 41% ~01m 04s
|++++++++++++++++++++++ | 42% ~01m 03s Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|++++++++++++++++++++++ | 43% ~01m 02s
|+++++++++++++++++++++++ | 44% ~01m 01s
|+++++++++++++++++++++++ | 45% ~60s
|++++++++++++++++++++++++ | 46% ~59s
|++++++++++++++++++++++++ | 47% ~57s
|+++++++++++++++++++++++++ | 48% ~56s
|+++++++++++++++++++++++++ | 49% ~55s
|++++++++++++++++++++++++++ | 51% ~54s
|++++++++++++++++++++++++++ | 52% ~53s
|+++++++++++++++++++++++++++ | 53% ~51s
|+++++++++++++++++++++++++++ | 54% ~50s
|++++++++++++++++++++++++++++ | 55% ~49s
|++++++++++++++++++++++++++++ | 56% ~48s
|+++++++++++++++++++++++++++++ | 57% ~47s
|+++++++++++++++++++++++++++++ | 58% ~46s
|++++++++++++++++++++++++++++++ | 59% ~44s
|++++++++++++++++++++++++++++++ | 60% ~43s
|+++++++++++++++++++++++++++++++ | 61% ~42s
|+++++++++++++++++++++++++++++++ | 62% ~41s
|++++++++++++++++++++++++++++++++ | 63% ~40s
|++++++++++++++++++++++++++++++++ | 64% ~39s
|+++++++++++++++++++++++++++++++++ | 65% ~38s
|+++++++++++++++++++++++++++++++++ | 66% ~37s
|++++++++++++++++++++++++++++++++++ | 67% ~36s
|+++++++++++++++++++++++++++++++++++ | 68% ~35s
|+++++++++++++++++++++++++++++++++++ | 69% ~34s
|++++++++++++++++++++++++++++++++++++ | 70% ~33s
|++++++++++++++++++++++++++++++++++++ | 71% ~31s
|+++++++++++++++++++++++++++++++++++++ | 72% ~30s
|+++++++++++++++++++++++++++++++++++++ | 73% ~29s
|++++++++++++++++++++++++++++++++++++++ | 74% ~28s
|++++++++++++++++++++++++++++++++++++++ | 75% ~27s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~26s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~25s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~23s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~22s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~21s Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~20s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~19s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~18s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~17s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~16s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~14s Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~12s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 50s
| | 0 % ~calculating
|+ | 1 % ~01m 24s
|++ | 2 % ~01m 27s
|++ | 3 % ~01m 32s
|+++ | 4 % ~01m 31s Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 82-100 (Heart_Plate.Rmd)
|+++ | 5 % ~01m 33s
|++++ | 6 % ~01m 34s
|++++ | 7 % ~01m 32s
|+++++ | 8 % ~01m 30s
|+++++ | 9 % ~01m 27s
|++++++ | 11% ~01m 26s
|++++++ | 12% ~01m 24s
|+++++++ | 13% ~01m 22s
|+++++++ | 14% ~01m 21s
|++++++++ | 15% ~01m 20s
|++++++++ | 16% ~01m 19s
|+++++++++ | 17% ~01m 18s
|+++++++++ | 18% ~01m 17s
|++++++++++ | 19% ~01m 16s
|++++++++++ | 20% ~01m 16s
|+++++++++++ | 21% ~01m 15s
|++++++++++++ | 22% ~01m 14s
|++++++++++++ | 23% ~01m 14s
|+++++++++++++ | 24% ~01m 14s
|+++++++++++++ | 25% ~01m 13s
|++++++++++++++ | 26% ~01m 12s
|++++++++++++++ | 27% ~01m 12s
|+++++++++++++++ | 28% ~01m 11s
|+++++++++++++++ | 29% ~01m 10s
|++++++++++++++++ | 31% ~01m 09s
|++++++++++++++++ | 32% ~01m 08s
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|++++++++++++++++++++++ | 42% ~59s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 41s
| | 0 % ~calculating
|+ | 1 % ~01m 11s
|++ | 2 % ~01m 03s
|++ | 3 % ~59s
|+++ | 4 % ~56s
|+++ | 5 % ~55s
|++++ | 6 % ~54s
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tiss.markers %>% group_by(cluster) %>% top_n(10, avg_diff)Assigning cell type identity to clusters
At a coarse level, we can use canonical markers to match the unbiased clustering to known cell types:
# stash current cluster IDs
tiss <- StashIdent(object = tiss, save.name = "cluster.ids")
# enumerate current cluster IDs and the labels for them
cluster.ids <- c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
annotation <- c("epithelial cell of large intestine", "enterocyte of epithelium of large intestine", "epithelial cell of large intestine", "large intestine goblet cell", "large intestine goblet cell", "epithelial cell of large intestine", "enterocyte of epithelium of large intestine", "large intestine goblet cell", "large intestine goblet cell", "epithelial cell of large intestine", "large intestine goblet cell", "Brush cell of epithelium proper of large intestine", "enteroendocrine cell")
cell_ontology_id <- c("CL:0002253", "CL:0002071", "CL:0002253", "CL:1000320", "CL:1000320", "CL:0002253", "CL:0002071", "CL:1000320", "CL:1000320", "CL:0002253", "CL:1000320", "CL:0002203", "CL:0000164")
tiss@meta.data[,'annotation'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = annotation)
tiss@meta.data[,'cell_ontology_id'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_id)
tiss@meta.data[tiss@cell.names,'annotation'] <- as.character(tiss@meta.data$annotation)
tiss@meta.data[tiss@cell.names,'cell_ontology_id'] <- as.character(tiss@meta.data$cell_ontology_id)
TSNEPlot(object = tiss, do.label = TRUE, pt.size = 0.5, group.by='annotation')
Checking for batch effects
Color by metadata, like plate barcode, to check for batch effects.
TSNEPlot(object = tiss, do.return = TRUE, group.by = "plate.barcode")
Print a table showing the count of cells in each identity category from each plate.
table(as.character(tiss@ident), as.character(tiss@meta.data$plate.barcode))
MAA000583 MAA000611 MAA000612 MAA000870 MAA000871 MAA000909 MAA000937 MAA001539 MAA001632 MAA001869 MAA001871 MAA001872
0 11 113 89 49 167 173 9 23 55 30 100 104
1 0 80 73 1 75 89 8 0 86 86 80 82
10 0 0 0 0 1 0 0 2 1 109 7 0
11 0 1 3 2 11 5 3 0 2 6 9 10
12 0 4 1 4 4 3 1 4 2 2 0 0
2 6 90 102 9 37 20 23 3 131 13 68 73
3 0 22 20 4 30 12 5 1 51 19 23 31
4 72 0 0 64 0 0 74 0 0 0 0 0
5 59 0 1 47 1 0 70 2 0 0 0 0
6 65 0 0 32 1 0 26 20 0 0 0 0
7 1 0 0 1 0 0 0 8 0 0 0 0
8 12 1 0 30 2 1 11 61 1 1 1 0
9 0 1 0 9 0 0 1 106 0 0 0 3
MAA001873 MAA001875
0 37 29
1 2 0
10 12 0
11 1 2
12 0 0
2 0 12
3 9 4
4 0 12
5 1 35
6 20 36
7 145 1
8 10 9
9 4 8When you save the annotated tissue, please give it a name.
filename = here('00_data_ingest', 'tissue_seurat_robj',
paste0(tissue_of_interest, "_seurat_tiss.Robj"))
print(filename)
save(tiss, file=filename)Export the final metadata
So that Biohub can easily combine all your annotations, please export them as a simple csv.
head(tiss@meta.data)filename = here('00_data_ingest', 'tissue_annotation_csv',
paste0(tissue_of_interest, "_annotation.csv"))
write.csv(tiss@meta.data[,c('plate.barcode','annotation','cell_ontology_id')], file=filename)---
title: "Colon FACS Notebook"
output:
  html_document: default
  html_notebook: default
---

Enter the directory of the maca folder on your drive and the name of the tissue you want to analyze.

```{r}
tissue_of_interest = "Colon"
```

Load the requisite packages and some additional helper functions.

```{r}
library(here)
library(useful)
library(Seurat)
library(dplyr)
library(Matrix)

save_dir = here('00_data_ingest', 'tissue_robj')
```



```{r}
# read the metadata to get the plates we want
plate_metadata_filename = here('00_data_ingest', 'facs_raw_data', 'metadata_FACS.csv')

plate_metadata <- read.csv(plate_metadata_filename, sep=",", header = TRUE)
colnames(plate_metadata)[1] <- "plate.barcode"
plate_metadata
```

Subset the metadata on the tissue.

```{r}
tissue_plates = filter(plate_metadata, tissue == tissue_of_interest)[,c('plate.barcode','tissue','subtissue','mouse.sex')]
tissue_plates
```

Load the read count data.
```{r}
#Load the gene names and set the metadata columns by opening the first file
filename = here('00_data_ingest', 'facs_raw_data', 'FACS', paste0(tissue_of_interest, '-counts.csv'))

raw.data = read.csv(filename, sep=",", row.names=1)
# raw.data = data.frame(row.names = rownames(raw.data))
corner(raw.data)
```
Make a vector of plate barcodes for each cell

```{r}
plate.barcodes = lapply(colnames(raw.data), function(x) strsplit(strsplit(x, "_")[[1]][1], '.', fixed=TRUE)[[1]][2])
head(plate.barcodes)
```

Use only the metadata rows corresponding to Bladder plates. Make a plate barcode dataframe to "expand" the per-plate metadata to be per-cell.

```{r}
head(raw.data)
```

```{r}
barcode.df = t.data.frame(as.data.frame(plate.barcodes))

rownames(barcode.df) = colnames(raw.data)
colnames(barcode.df) = c('plate.barcode')
head(barcode.df)

rnames = row.names(barcode.df)
meta.data <- merge(barcode.df, plate_metadata, by='plate.barcode', sort = F)
row.names(meta.data) <- rnames

# Sort cells by plate barcode because that's how the data was originally
meta.data = meta.data[order(meta.data$plate.barcode), ]
raw.data = raw.data[, rownames(meta.data)]
head(raw.data)
```


Process the raw data and load it into the Seurat object.

```{r}
# Find ERCC's, compute the percent ERCC, and drop them from the raw data.
erccs <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = TRUE)
percent.ercc <- Matrix::colSums(raw.data[erccs, ])/Matrix::colSums(raw.data)
ercc.index <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = FALSE)
raw.data <- raw.data[-ercc.index,]

# Create the Seurat object with all the data
tiss <- CreateSeuratObject(raw.data = raw.data, project = tissue_of_interest, 
                    min.cells = 5, min.genes = 5)

tiss <- AddMetaData(object = tiss, meta.data)
tiss <- AddMetaData(object = tiss, percent.ercc, col.name = "percent.ercc")
# Change default name for sums of counts from nUMI to nReads
colnames(tiss@meta.data)[colnames(tiss@meta.data) == 'nUMI'] <- 'nReads'

# Create metadata columns for annotations and subannotations
tiss@meta.data[,'annotation'] <- NA
tiss@meta.data[,'subannotation'] <- NA
```


Calculate percent ribosomal genes.

```{r}
ribo.genes <- grep(pattern = "^Rp[sl][[:digit:]]", x = rownames(x = tiss@data), value = TRUE)
percent.ribo <- Matrix::colSums(tiss@raw.data[ribo.genes, ])/Matrix::colSums(tiss@raw.data)
tiss <- AddMetaData(object = tiss, metadata = percent.ribo, col.name = "percent.ribo")
```

A sanity check: genes per cell vs reads per cell.

```{r}
GenePlot(object = tiss, gene1 = "nReads", gene2 = "nGene", use.raw=T)
```

Filter out cells with few reads and few genes.

```{r}
tiss <- FilterCells(object = tiss, subset.names = c("nGene", "nReads"), 
    low.thresholds = c(500, 50000), high.thresholds = c(25000, 2000000))
```


Normalize the data, then regress out correlation with total reads
```{r}
tiss <- NormalizeData(object = tiss)
tiss <- ScaleData(object = tiss, vars.to.regress = c("nReads", "percent.ribo","Rn45s"))
tiss <- FindVariableGenes(object = tiss, do.plot = TRUE, x.high.cutoff = Inf, y.cutoff = 0.5)
```


Run Principal Component Analysis.
```{r}
tiss <- RunPCA(object = tiss, do.print = FALSE)
tiss <- ProjectPCA(object = tiss, do.print = FALSE)
```

```{r, echo=FALSE, fig.height=4, fig.width=8}
PCHeatmap(object = tiss, pc.use = 1:3, cells.use = 500, do.balanced = TRUE, label.columns = FALSE, num.genes = 8)
```

Later on (in FindClusters and TSNE) you will pick a number of principal components to use. This has the effect of keeping the major directions of variation in the data and, ideally, supressing noise. There is no correct answer to the number to use, but a decent rule of thumb is to go until the plot plateaus.

```{r}
PCElbowPlot(object = tiss)
```

Choose the number of principal components to use.
```{r}
# Set number of principal components. 
n.pcs = 20
```


The clustering is performed based on a nearest neighbors graph. Cells that have similar expression will be joined together. The Louvain algorithm looks for groups of cells with high modularity--more connections within the group than between groups. The resolution parameter determines the scale...higher resolution will give more clusters, lower resolution will give fewer.

For the top-level clustering, aim to under-cluster instead of over-cluster. It will be easy to subset groups and further analyze them below.

```{r}
# Set resolution 
res.used <- 0.5

tiss <- FindClusters(object = tiss, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
```

To visualize 
```{r}
# If cells are too spread out, you can raise the perplexity. If you have few cells, try a lower perplexity (but never less than 10).
tiss <- RunTSNE(object = tiss, dims.use = 1:n.pcs, seed.use = 10, perplexity=30)
```

```{r}
# note that you can set do.label=T to help label individual clusters
TSNEPlot(object = tiss, do.label = T)
```

Check expression of genes of interset.

```{r}
#{r, echo=FALSE, fig.height=16, fig.width=12}
genes_to_check = c('Lgr5', 'Krt20', 'Atoh1', 'Chga', 'Dclk1', 'Mki67')
#genes_to_check = c('Alb', 'Cyp2f2', 'Cyp2e1', 'Hamp')

#FeaturePlot(tiss, genes_to_check, pt.size = 1, nCol = 3)
FeaturePlot(tiss, genes_to_check, pt.size = 1)
```

Dotplots let you see the intensity of exppression and the fraction of cells expressing for each of your genes of interest.

```{r}
# To change the y-axis to show raw counts, add use.raw = T.
VlnPlot(tiss, genes_to_check)
```

```{r}
# To change the y-axis to show raw counts, add use.raw = T.
VlnPlot(tiss, 'Lgr5')
```

```{r}
# To get plots of two genes, add use.raw = T.
GenePlot(tiss, 'Krt20', 'Atoh1')
```

```{r, echo=FALSE, fig.height=4, fig.width=8}
# To change the y-axis to show raw counts, add use.raw = T.
DotPlot(tiss, genes_to_check, plot.legend = T)
```

How big are the clusters?
```{r}
table(tiss@ident)
```

Which markers identify a specific cluster?

```{r}
clust.markers <- FindMarkers(object = tiss, ident.1 = 0, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
```

```{r}
print(x = head(x= clust.markers, n = 10))
```

You can also compute all markers for all clusters at once. This may take some time.
```{r}
tiss.markers <- FindAllMarkers(object = tiss, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
```

Display the top markers you computed above.
```{r}
tiss.markers %>% group_by(cluster) %>% top_n(10, avg_diff)
```

## Assigning cell type identity to clusters

At a coarse level, we can use canonical markers to match the unbiased clustering to known cell types:

```{r}
# stash current cluster IDs
tiss <- StashIdent(object = tiss, save.name = "cluster.ids")

# enumerate current cluster IDs and the labels for them
cluster.ids <- c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
annotation <- c("epithelial cell of large intestine", "enterocyte of epithelium of large intestine", "epithelial cell of large intestine", "large intestine goblet cell", "large intestine goblet cell", "epithelial cell of large intestine", "enterocyte of epithelium of large intestine", "large intestine goblet cell", "large intestine goblet cell", "epithelial cell of large intestine", "large intestine goblet cell", "Brush cell of epithelium proper of large intestine", "enteroendocrine cell")
cell_ontology_id <- c("CL:0002253", "CL:0002071", "CL:0002253", "CL:1000320", "CL:1000320", "CL:0002253", "CL:0002071", "CL:1000320", "CL:1000320", "CL:0002253", "CL:1000320", "CL:0002203", "CL:0000164")


tiss@meta.data[,'annotation'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = annotation)
tiss@meta.data[,'cell_ontology_id'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_id)

tiss@meta.data[tiss@cell.names,'annotation'] <- as.character(tiss@meta.data$annotation)
tiss@meta.data[tiss@cell.names,'cell_ontology_id'] <- as.character(tiss@meta.data$cell_ontology_id)


TSNEPlot(object = tiss, do.label = TRUE, pt.size = 0.5, group.by='annotation')
```


## Checking for batch effects


Color by metadata, like plate barcode, to check for batch effects.
```{r}
TSNEPlot(object = tiss, do.return = TRUE, group.by = "plate.barcode")
```

Print a table showing the count of cells in each identity category from each plate.

```{r}
table(as.character(tiss@ident), as.character(tiss@meta.data$plate.barcode))
```


When you save the annotated tissue, please give it a name.

```{r}
filename = here('00_data_ingest', 'tissue_seurat_robj', 
                     paste0(tissue_of_interest, "_seurat_tiss.Robj"))
print(filename)
save(tiss, file=filename)
```


# Export the final metadata

So that Biohub can easily combine all your annotations, please export them as a simple csv.

```{r}
head(tiss@meta.data)
```


```{r}
filename = here('00_data_ingest', 'tissue_annotation_csv', 
                     paste0(tissue_of_interest, "_annotation.csv"))
write.csv(tiss@meta.data[,c('plate.barcode','annotation','cell_ontology_id')], file=filename)
```


